1. Technical Field
The present invention relates to data processing and, in particular, to fraud detection. Still more particularly, the present invention provides a method, apparatus, and program for detecting fraud using a system of dynamic, data-driven models.
2. Description of Related Art
Most banks throughout the world experience fraudulent behavior. One of the most common example of this behavior is credit card fraud where either a credit card number or even a physical credit card is stolen thereby forcing the bank to absorb an unauthorized charge. A credit card number is at risk of being stolen every time a salesperson or waitperson takes a card out of the sight of the customer, every time a credit card number is spoken over the telephone, or every time a purchase is made online. There have been recent attacks on electronic commerce Web sites in which thousands of credit card numbers were stolen at once. Some unscrupulous people even dig through garbage bins looking for credit card numbers.
Once a credit card number is stolen and compromised, it may be used in many ways. Some thieves make many small purchases hoping they go unnoticed. Others go on one wild shopping spree. The only protection the credit card owner and the issuer have is the signature. For many years, this was an accepted risk. Merchants agreed to accept credit card payments for the convenience of their customers knowing that the customer's signature was their only protection against fraud. However, consumers have grown more and more comfortable with electronic commerce and, as a consequence, credit card fraud has reached alarming levels. For example, a large bank in Caracas Venezuela was losing over one million dollars a month due to various fraudulent activity, including fraudulent credit card charges. For every instance of credit card fraud, either the account holder, the merchant, or the account issuer has to eat the cost, as in the above example.
Other forms of fraudulent activity may also be used, one example being account kiting. An account holder will write a check drawn against a first account at a first financial institution and deposit it into a second account at a second financial institution. Before the funds are collected from the first financial institution, a check is drawn against the balance of the second account and deposited into the first account to cover the amount of the check. As the account holder continues this process, checks are drawn against balances in both accounts. Typically, the balances escalate because the kiter writes the check for more than the amount of that clearing, and will keep the excess amount in cash. The kiter may then repeat this process indefinitely, increasing the amount of the checks and withdrawing a substantial amount of cash each time.
Enterprises other than banks also experience fraudulent behavior. Employees may file fraudulent expense reports, for example. Many solutions have been proposed for preventing fraud. However, those who commit fraud spend just as much effort to circumvent these solutions. While fraud may be impeded using digital cryptography, personal identification numbers, and other security measures, fraudulent behavior will always be an ongoing concern.
Solutions have also been proposed to detect fraud. Many of these solutions use static data-driven and statistical models, based on historical data, to detect behavior that is outside the account holder's usual behavior. However, modeling an account holder's total behavior is too complex and computation intensive. These solutions are typically ineffective. Even if the proper controls and procedures are in place, they are not properly and uniformly enforced. Many banks and other institutions still have fraud occurring throughout the enterprise. Most of the time they do not have the capability or expertise to identify, quantify, eliminate, or even minimize this undesirable behavior.